Brain parcellation choice affects disease-related topology differences increasingly from global to local network levels

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

The brain's complex distributed dynamics are typically quantified using a limited set of manually selected statistical properties, leaving the possibility that alternative dynamical properties may outperform those reported for a given application. Here, we address this limitation by systematically comparing diverse, interpretable features of both intra-regional activity and inter-regional functional coupling from resting-state functional magnetic resonance imaging (rs-fMRI) data, demonstrating our method using case-control comparisons of four neuropsychiatric disorders. Our findings generally support the use of linear time-series analysis techniques for rs-fMRI case-control analyses, while also identifying new ways to quantify informative dynamical fMRI structures. While simple statistical representations of fMRI dynamics performed surprisingly well (e.g., properties within a single brain region), combining intra-regional properties with inter-regional coupling generally improved performance, underscoring the distributed, multifaceted changes to fMRI dynamics in neuropsychiatric disorders. The comprehensive, data-driven method introduced here enables systematic identification and interpretation of quantitative dynamical signatures of multivariate time-series data, with applicability beyond neuroimaging to diverse scientific problems involving complex time-varying systems.

Searching Reproducible Brain Features using NeuroMark: Templates for Different Age Populations and Imaging Modalities

A primary challenge to the data-driven analysis is the balance between poor generalizability of population-based research and characterizing more subject-, study- and population-specific variability. We previously introduced a fully automated spatially constrained independent component analysis (ICA) framework called NeuroMark and its functional MRI (fMRI) template. NeuroMark has been successfully applied in numerous studies, identifying brain markers reproducible across datasets and disorders. The first NeuroMark template was constructed based on young adult cohorts. We recently expanded on this initiative by creating a standardized normative multi-spatial-scale functional template using over 100,000 subjects, aiming to improve generalizability and comparability across studies involving diverse cohorts. While a unified template across the lifespan is desirable, a comprehensive investigation of the similarities and differences between components from different age populations might help systematically transform our understanding of the human brain by revealing the most well-replicated and variable network features throughout the lifespan. In this work, we introduced two significant expansions of NeuroMark templates first by generating replicable fMRI templates for infants, adolescents, and aging cohorts, and second by incorporating structural MRI (sMRI) and diffusion MRI (dMRI) modalities. Specifically, we built spatiotemporal fMRI templates based on 6,000 resting-state scans from four datasets. This is the first attempt to create robust ICA templates covering dynamic brain development across the lifespan. For the sMRI and dMRI data, we used two large publicly available datasets including more than 30,000 scans to build reliable templates. We employed a spatial similarity analysis to identify replicable templates and investigate the degree to which unique and similar patterns are reflective in different age populations. Our results suggest remarkably high similarity of the resulting adapted components, even across extreme age differences. With the new templates, the NeuroMark framework allows us to perform age-specific adaptations and to capture features adaptable to each modality, therefore facilitating biomarker identification across brain disorders. In sum, the present work demonstrates the generalizability of NeuroMark templates and suggests the potential of new templates to boost accuracy in mental health research and advance our understanding of lifespan and cross-modal alterations.

Predictability of intelligence and age from structural connectomes

In this study, structural images of 1048 healthy subjects from the Human Connectome Project Young Adult study and 94 from ADNI-3 study were processed by an in-house tractography pipeline and analyzed together with pre-processed data of the same subjects from braingraph.org. Whole brain structural connectome features were used to build a simple correlation-based regression machine learning model to predict intelligence and age of healthy subjects. Our results showed that different forms of intelligence as well as age are predictable to a certain degree from diffusion tensor imaging detecting anatomical fiber tracts in the living human brain. Though we did not identify significant differences in the prediction capability for the investigated features depending on the imaging feature extraction method, we did find that crystallized intelligence was consistently better predictable than fluid intelligence from structural connectivity data through all datasets. Our findings suggest a practical and scalable processing and analysis framework to explore broader research topics employing brain MR imaging.

Comprehensive elucidation of resting-state functional connectivity in anorexia nervosa by a multicenter cross-sectional study

BACKGROUND

Previous research on the changes in resting-state functional connectivity (rsFC) in anorexia nervosa (AN) has been limited by an insufficient sample size, which reduced the reliability of the results and made it difficult to set the whole brain as regions of interest (ROIs).

METHODS

We analyzed functional magnetic resonance imaging data from 114 female AN patients and 135 healthy controls (HC) and obtained self-reported psychological scales, including eating disorder examination questionnaire 6.0. One hundred sixty-four cortical, subcortical, cerebellar, and network parcellation regions were considered as ROIs. We calculated the ROI-to-ROI rsFCs and performed group comparisons.

RESULTS

Compared to HC, AN patients showed 12 stronger rsFCs mainly in regions containing dorsolateral prefrontal cortex (DLPFC), and 33 weaker rsFCs primarily in regions containing cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between anterior cingulate cortex (ACC) and thalamus (p < 0.01, false discovery rate [FDR] correction). Comparisons between AN subtypes showed that there were stronger rsFCs between right lingual gyrus and right supracalcarine cortex and between left temporal occipital fusiform cortex and medial part of visual network in the restricting type compared to the binge/purging type (p < 0.01, FDR correction).

CONCLUSION

Stronger rsFCs in regions containing mainly DLPFC, and weaker rsFCs in regions containing primarily cerebellum, within temporal lobe, between posterior fusiform cortex and lateral part of visual network, and between ACC and thalamus, may represent categorical diagnostic markers discriminating AN patients from HC.

Multimodal and multiscale evidence for network-based cortical thinning in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is associated with widespread, irregular cortical thickness (CT) reductions across the brain. However, little is known regarding mechanisms that govern spatial distribution of the reductions.

METHODS

We combined multimodal MRI and genetic, cytoarchitectonic and chemoarchitectonic data to examine structural covariance, functional synchronization, gene co-expression, cytoarchitectonic similarity and chemoarchitectonic covariance between regions atrophied in MDD.

RESULTS

Regions atrophied in MDD were associated with significantly higher structural covariance, functional synchronization, gene co-expression and chemoarchitectonic covariance. These results were robust against methodological variations in brain parcellation and null model, reproducible in patients and controls, and independent of age at onset of MDD. Despite no significant differences in the cytoarchitectonic similarity, MDD-related CT reductions were susceptible to specific cytoarchitectonic class of association cortex. Further, we found that nodal shortest path lengths to disease epicenters derived from structural (right supramarginal gyrus) and chemoarchitectonic covariance (right sulcus intermedius primus) networks of healthy brains were correlated with the extent to which a region was atrophied in MDD, supporting the transneuronal spread hypothesis that regions closer to the epicenters are more susceptible to MDD. Finally, we showed that structural covariance and functional synchronization among regions atrophied in MDD were mainly related to genes enriched in metabolic and membrane-related processes, driven by genes in excitatory neurons, and associated with specific neurotransmitter transporters and receptors.

CONCLUSIONS

Altogether, our findings provide empirical evidence for and genetic and molecular insights into connectivity-constrained CT thinning in MDD.

Central Autonomic Network Alterations in Anorexia Nervosa Following Peripheral Adrenergic Stimulation

BACKGROUND

Anorexia nervosa (AN) is characterized by low body weight, disturbed eating, body image disturbance, anxiety, and interoceptive dysfunction. However, the neural processes underlying these dysfunctions in AN are unclear. This investigation combined an interoceptive pharmacological probe, the peripheral β-adrenergic agonist isoproterenol, with resting-state functional magnetic resonance imaging to examine whether individuals with AN relative to healthy comparison participants show dysregulated neural coupling in central autonomic network brain regions.

METHODS

Resting-state functional magnetic resonance imaging was performed in 23 weight-restored female participants with AN and 23 age- and body mass index-matched healthy comparison participants before and after receiving isoproterenol infusions. Whole-brain functional connectivity (FC) changes were examined using central autonomic network seeds in the amygdala, anterior insular cortex, posterior cingulate cortex, and ventromedial prefrontal cortex after performing physiological noise correction procedures.

RESULTS

Relative to healthy comparison participants, adrenergic stimulation caused widespread FC reductions in the AN group between central autonomic network regions and motor, premotor, frontal, parietal, and visual brain regions. Across both groups, these FC changes were inversely associated with trait anxiety (State-Trait Anxiety Inventory-Trait), trait depression (9-item Patient Health Questionnaire), and negative body image perception (Body Shape Questionnaire) measures, but not with changes in resting heart rate. These results were not accounted for by baseline group FC differences.

CONCLUSIONS

Weight-restored females with AN show a widespread state-dependent disruption of signaling between central autonomic, frontoparietal, and sensorimotor brain networks that facilitate interoceptive representation and visceromotor regulation. Additionally, trait associations between central autonomic network regions and these other brain networks suggest that dysfunctional processing of interoceptive signaling may contribute to affective and body image disturbance in AN.

Disrupted longitudinal restoration of brain connectivity during weight normalization in severe anorexia nervosa

Altered intrinsic brain connectivity of patients with anorexia nervosa has been observed in the acute phase of the disorder, but it remains unclear to what extent these alterations recover during weight normalization. In this study, we used functional imaging data from three time points to probe longitudinal changes in intrinsic connectivity patterns in patients with severe anorexia nervosa (BMI ≤ 15.5 kg/m²) over the course of weight normalization. At three distinct stages of inpatient treatment, we examined resting-state functional connectivity in 27 women with severe anorexia nervosa and 40 closely matched healthy controls. Using network-based statistics and graph-theoretic measures, we examined differences in global network strength, subnetworks with altered intrinsic connectivity, and global network topology. Patients with severe anorexia nervosa showed weakened intrinsic connectivity and altered network topology which did not recover during treatment. The persistent disruption of brain networks suggests sustained alterations of information processing in weight-recovered severe anorexia nervosa.

Model-based whole-brain perturbational landscape of neurodegenerative diseases

The treatment of neurodegenerative diseases is hindered by lack of interventions capable of steering multimodal whole-brain dynamics towards patterns indicative of preserved brain health. To address this problem, we combined deep learning with a model capable of reproducing whole-brain functional connectivity in patients diagnosed with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). These models included disease-specific atrophy maps as priors to modulate local parameters, revealing increased stability of hippocampal and insular dynamics as signatures of brain atrophy in AD and bvFTD, respectively. Using variational autoencoders, we visualized different pathologies and their severity as the evolution of trajectories in a low-dimensional latent space. Finally, we perturbed the model to reveal key AD- and bvFTD-specific regions to induce transitions from pathological to healthy brain states. Overall, we obtained novel insights on disease progression and control by means of external stimulation, while identifying dynamical mechanisms that underlie functional alterations in neurodegeneration.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

DeepEZ: A Graph Convolutional Network for Automated Epileptogenic Zone Localization From Resting-State fMRI Connectivity

OBJECTIVE

Epileptogenic zone (EZ) localization is a crucial step during diagnostic work up and therapeutic planning in medication refractory epilepsy. In this paper, we present the first deep learning approach to localize the EZ based on resting-state fMRI (rs-fMRI) data.

METHODS

Our network, called DeepEZ, uses a cascade of graph convolutions that emphasize signal propagation along expected anatomical pathways. We also integrate domain-specific information, such as an asymmetry term on the predicted EZ and a learned subject-specific bias to mitigate environmental confounds.

RESULTS

We validate DeepEZ on rs-fMRI collected from 14 patients with focal epilepsy at the University of Wisconsin Madison. Using cross validation, we demonstrate that DeepEZ achieves consistently high EZ localization performance (Accuracy: 0.88 ± 0.03; AUC: 0.73 ± 0.03) that far outstripped any of the baseline methods. This performance is notable given the variability in EZ locations and scanner type across the cohort.

CONCLUSION

Our results highlight the promise of using DeepEZ as an accurate and noninvasive therapeutic planning tool for medication refractory epilepsy.

SIGNIFICANCE

While prior work in EZ localization focused on identifying localized aberrant signatures, there is growing evidence that epileptic seizures affect inter-regional connectivity in the brain. DeepEZ allows clinicians to harness this information from noninvasive imaging that can easily be integrated into the existing clinical workflow.

Anorexia nervosa as a disorder of the subcortical-cortical interoceptive-self

PURPOSE

Anorexia nervosa (AN) is characterized by a diminished capacity in perceiving the physiological correlates of interoceptive sensations, namely bodily self-consciousness. Given the neural division of self-processing into interoceptive-, exteroceptive- and mental-self, we hypothesize neural deficits in the interoceptive-processing regions in AN.

METHODS

To prove this, we reviewed resting state (rs), task and rest-task studies in AN literature.

RESULTS

Neuronal data demonstrate the following in AN: (i) decreased rs-functional connectivity (rsFC) of subcortical-cortical midline structures (SCMS); (ii) reduced rsFC between medial (default-mode network/DMN and salience network/SN) and lateral (executive-control network/ECN) cortical regions; (iii) decreased rsFC in mainly the regions of the interoceptive-self; (iv) altered activity with overall increased activity in response to sensory/body image stimuli, especially in the regions of the interoceptive-self; (v) lack of a clear task-related distinction between own's and others' body image.

CONCLUSION

These data may indicate that rs-hypoconnectivity between SCMS, as neural correlate of a reduced intero-exteroceptive integration resulting in self-objectification, might be linked to overall increased activity in interoceptive regions during sensory/body image stimuli in AN, engendering an "anxious bodily self."

LEVEL OF EVIDENCE

I: Systematic review.

Predicting the severity of internet gaming disorder with resting-state brain features: A multi-voxel pattern analysis

BACKGROUND

Internet gaming disorder (IGD) has become a worldwide mental health concern; however, the neural mechanism underlying this disorder remains unclear. Multivoxel pattern analysis (MVPA), a newly developed data-driven approach, can be used to investigate the neural features of IGD based on massive neural data.

METHODS

Resting-state fMRI data from four hundred and two participants with varying levels of IGD severity were recruited. Regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF) were calculated and subsequently decoded by applying MVPA. The highly weighted regions in both predictive models were selected as regions of interest for further graph theory and Granger causality analysis (GCA) to explore how they affect IGD severity.

RESULTS

The results revealed that the neural patterns of ReHo and ALFF can independently and significantly predict IGD severity. The highly weighted regions that contributed to both predictive models were the right precentral gyrus and left postcentral gyrus. Moreover, topological properties of the right precentral gyrus were significantly correlated with IGD severity; further GCA revealed effective connectivity from the right precentral gyrus to left precentral gyrus and dorsal anterior cingulate cortex, both of which were significantly associated with IGD severity.

CONCLUSIONS

The present study demonstrated that IGD has distinctive neural patterns, and this pattern could be found by machine learning. In addition, the neural features in the right precentral gyrus play a key role in predicting IGD severity. The current study revealed the neural features of IGD and provided a potential target for IGD interventions using brain modulation.

Enhancing Cortical Network-level Participation Coefficient as a Potential Mechanism for Transfer in Cognitive Training in aMCI

Effective cognitive training must improve cognition beyond the trained domain (show a transfer effect) and be applicable to dementia-risk populations, e.g., amnesic mild cognitive impairment (aMCI). Theories suggest training should target processes that 1) show robust engagement, 2) are domain-general, and 3) reflect long-lasting changes in brain organization. Brain regions that connect to many different networks (i.e., show high participation coefficient; PC) are known to support integration. This capacity is 1) relatively preserved in aMCI, 2) required across a wide range of cognitive domains, and 3) trait-like. In 49 individuals with aMCI that completed a 6-week visual speed of processing training (VSOP) and 28 active controls, enhancement in PC was significantly more related to transfer to working memory at global and network levels in VSOP compared to controls, particularly in networks with many high-PC nodes. This suggests that enhancing brain integration may provide a target for developing effective cognitive training.

Patterns of a structural covariance network associated with dispositional optimism during late adolescence

Dispositional optimism (hereinafter, optimism), as a vital character strength, reflects the tendency to hold generalized positive expectancies for future outcomes. A great number of studies have consistently shown the importance of optimism to a spectrum of physical and mental health outcomes. However, less attention has been given to the intrinsic neurodevelopmental patterns associated with interindividual differences in optimism. Here, we investigated this important question in a large sample comprising 231 healthy adolescents (16-20 years old) via structural magnetic resonance imaging and behavioral tests. We constructed individual structural covariance networks based on cortical gyrification using a recent novel approach combining probability density estimation and Kullback-Leibler divergence and estimated global (global efficiency, local efficiency and small-worldness) and regional (betweenness centrality) properties of these constructed networks using graph theoretical analysis. Partial correlations adjusted for age, sex and estimated total intracranial volume showed that optimism was positively related to global and local efficiency but not small-worldness. Partial least squares correlations indicated that optimism was positively linked to a pronounced betweenness centrality pattern, in which twelve cognition-, emotion-, and motivation-related regions made robust and reliable contributions. These findings remained basically consistent after additionally controlling for family socioeconomic status and showed significant correlations with optimism scores from 2.5 years before, which replicated the main findings. The current work, for the first time, delineated characteristics of the cortical gyrification covariance network associated with optimism, extending previous neurobiological understandings of optimism, which may navigate the development of interventions on a neural network level aimed at raising optimism.

Complex functional brain network properties in anorexia nervosa

BACKGROUND

Anorexia nervosa (AN) is a disorder characterized by an incapacitating fear of weight gain and by a disturbance in the way the body is experienced, facets that motivate dangerous weight loss behaviors. Multimodal neuroimaging studies highlight atypical neural activity in brain networks involved in interoceptive awareness and reward processing.

METHODS

The current study used resting-state neuroimaging to model the architecture of large-scale functional brain networks and characterize network properties of individual brain regions to clinical measures. Resting-state neuroimaging was conducted in 62 adolescents, 22 (21 female) with a history of AN and 40 (39 female) healthy controls (HCs). Sensorimotor and basal ganglia regions, as part of a 165-region whole-brain network, were investigated. Subject-specific functional brain networks were computed to index centrality. A contrast analysis within the general linear model covarying for age was performed. Correlations between network properties and behavioral measures were conducted (significance q < .05).

RESULTS

Compared to HCs, AN had lower connectivity from sensorimotor regions, and greater connectivity from the left caudate nucleus to the right postcentral gyrus. AN demonstrated lower sensorimotor centrality, but higher basal ganglia centrality. Sensorimotor connectivity dyads and centrality exhibited negative correlations with body dissatisfaction and drive for thinness, two essential features of AN.

CONCLUSIONS

These findings suggest that AN is associated with greater communication from the basal ganglia, and lower information propagation in sensorimotor cortices. This is consistent with the clinical presentation of AN, where individuals exhibit patterns of rigid habitual behavior that is not responsive to bodily needs, and seem "disconnected" from their bodies.

Brain networks in eating disorders: a systematic review of graph theory studies

PURPOSE

Recent evidence from neuroimaging research has shown that eating disorders (EDs) are characterized by alterations in interconnected neural systems, whose characteristics can be usefully described by connectomics tools. The present paper aimed to review the neuroimaging literature in EDs employing connectomic tools, and, specifically, graph theory analysis.

METHODS

A systematic review of the literature was conducted to identify studies employing graph theory analysis on patients with eating disorders published before the 22nd of June 2020.

RESULTS

Twelve studies were included in the systematic review. Ten of them address anorexia nervosa (AN) (AN = 199; acute AN = 85, weight recovered AN with acute diagnosis = 24; fully recovered AN = 90). The remaining two articles address patients with bulimia nervosa (BN) (BN = 48). Global and regional unbalance in segregation and integration properties were described in both disorders.

DISCUSSION

The literature concerning the use of connectomics tools in EDs evidenced the presence of alterations in the topological characteristics of brain networks at a global and at a regional level. Changes in local characteristics involve areas that have been demonstrated to be crucial in the neurobiology and pathophysiology of EDs. Regional imbalances in network properties seem to reflect on global patterns.

LEVEL OF EVIDENCE

Level I, systematic review.

Inside information: Systematic within-node functional connectivity changes observed across tasks or groups

Mapping the human connectome and understanding its relationship to brain function holds tremendous clinical potential. The connectome has two fundamental components: the nodes and the sconnections between them. While much attention has been given to deriving atlases and measuring the connections between nodes, there have been no studies examining the networks within nodes. Here we demonstrate that each node contains significant connectivity information, that varies systematically across task-induced states and subjects, such that measures based on these variations can be used to classify tasks and identify subjects. The results are not specific for any particular atlas but hold across different atlas resolutions. To date, studies examining changes in connectivity have focused on edge changes and assumed there is no useful information within nodes. Our findings illustrate that for typical atlases, within-node changes can be significant and may account for a substantial fraction of the variance currently attributed to edge changes .

Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks

Localizing the eloquent cortex is a crucial part of presurgical planning. While invasive mapping is the gold standard, there is increasing interest in using noninvasive fMRI to shorten and improve the process. However, many surgical patients cannot adequately perform task-based fMRI protocols. Resting-state fMRI has emerged as an alternative modality, but automated eloquent cortex localization remains an open challenge. In this paper, we develop a novel deep learning architecture to simultaneously identify language and primary motor cortex from rs-fMRI connectivity. Our approach uses the representational power of convolutional neural networks alongside the generalization power of multi-task learning to find a shared representation between the eloquent subnetworks. We validate our method on data from the publicly available Human Connectome Project and on a brain tumor dataset acquired at the Johns Hopkins Hospital. We compare our method against feature-based machine learning approaches and a fully-connected deep learning model that does not account for the shared network organization of the data. Our model achieves significantly better performance than competing baselines. We also assess the generalizability and robustness of our method. Our results clearly demonstrate the advantages of our graph convolution architecture combined with multi-task learning and highlight the promise of using rs-fMRI as a presurgical mapping tool.

Small Blob Detector Using Bi-Threshold Constrained Adaptive Scales

Recent advances in medical imaging technology bring great promises for medicine practices. Imaging biomarkers are discovered to inform disease diagnosis, prognosis, and treatment assessment. Detecting and segmenting objects from images are often the first steps in quantitative measurement of these biomarkers. The challenges of detecting objects in images, particularly small objects known as blobs, include low image resolution, image noise and overlap among the blobs. This research proposes a Bi-Threshold Constrained Adaptive Scale (BTCAS) blob detector to uncover the relationship between the U-Net threshold and the Difference of Gaussian (DoG) scale to derive a multi-threshold, multi-scale small blob detector. With lower and upper bounds on the probability thresholds from U-Net, two binarized maps of the distance are rendered between blob centers. Each blob is transformed to a DoG space with an adaptively identified local optimum scale. A Hessian convexity map is rendered using the adaptive scale, and the under-segmentation typical of the U-Net is resolved. To validate the performance of the proposed BTCAS, a 3D simulated dataset (n = 20) of blobs, a 3D MRI dataset of human kidneys and a 3D MRI dataset of mouse kidneys, are studied. BTCAS is compared against four state-of-the-art methods: HDoG, U-Net with standard thresholding, U-Net with optimal thresholding, and UH-DoG using precision, recall, F-score, Dice and IoU. We conclude that BTCAS statistically outperforms the compared detectors.

Distinct functional and structural connections predict crystallised and fluid cognition in healthy adults

White matter pathways between neurons facilitate neuronal coactivation patterns in the brain. Insight into how these structural and functional connections underlie complex cognitive functions provides an important foundation with which to delineate disease‐related changes in cognitive functioning. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how functional and structural brain connectivity relate to cognition. Specifically, we evaluate the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities in 415 unrelated healthy young adults (202 females) from the Human Connectome Project. We report three main findings. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities. Taken together, our results suggest that functional and structural connectivity have unique relationships with crystallised and fluid cognition and, furthermore, studying both modalities provides a more comprehensive insight into the neural correlates of cognition.

Global Network Organization of the Fetal Functional Connectome

Recent advances in brain imaging have enabled non-invasive in vivo assessment of the fetal brain. Characterizing brain development in healthy fetuses provides baseline measures for identifying deviations in brain function in high-risk clinical groups. We examined 110 resting state MRI data sets from fetuses at 19 to 40 weeks' gestation. Using graph-theoretic techniques, we characterized global organizational features of the fetal functional connectome and their prenatal trajectories. Topological features related to network integration (i.e., global efficiency) and segregation (i.e., clustering) were assessed. Fetal networks exhibited small-world topology, showing high clustering and short average path length relative to reference networks. Likewise, fetal networks' quantitative small world indices met criteria for small-worldness (σ > 1, ω = [-0.5 0.5]). Along with this, fetal networks demonstrated global and local efficiency, economy, and modularity. A right-tailed degree distribution, suggesting the presence of central areas that are more highly connected to other regions, was also observed. Metrics, however, were not static during gestation; measures associated with segregation-local efficiency and modularity-decreased with advancing gestational age. Altogether, these suggest that the neural circuitry underpinning the brain's ability to segregate and integrate information exists as early as the late 2nd trimester of pregnancy and reorganizes during the prenatal period. Significance statement. Mounting evidence for the fetal origins of some neurodevelopmental disorders underscores the importance of identifying features of healthy fetal brain functional development. Alterations in prenatal brain connectomics may serve as early markers for identifying fetal-onset neurodevelopmental disorders, which in turn provide improved surveillance of at-risk fetuses and support the initiation of early interventions.

Cortical thinning and associated connectivity changes in patients with anorexia nervosa

Structural brain abnormalities are a consistent finding in anorexia nervosa (AN) and proposed as a state biomarker of the disorder. Yet little is known about how regional structural changes affect intrinsic resting-state functional brain connectivity (rsFC). Using a cross-sectional, multimodal imaging approach, we investigated the association between regional cortical thickness abnormalities and rsFC in AN. Twenty-two acute AN patients and twenty-six age- and gender-matched healthy controls underwent a resting-state functional magnetic resonance imaging scan and cognitive tests. We performed group comparisons of whole-brain cortical thickness, seed-based rsFC, and network-based statistical (NBS) analyses. AN patients showed cortical thinning in the precuneus and inferior parietal lobules, regions involved in visuospatial memory and imagery. Cortical thickness in the precuneus correlated with nutritional state and cognitive functions in AN, strengthening the evidence for a critical role of this region in the disorder. Cortical thinning was accompanied by functional connectivity reductions in major brain networks, namely default mode, sensorimotor and visual networks. Similar to the seed-based approach, the NBS analysis revealed a single network of reduced functional connectivity in patients, comprising mainly sensorimotor- occipital regions. Our findings provide evidence that structural and functional brain abnormalities in AN are confined to specific regions and networks involved in visuospatial and somatosensory processing. We show that structural changes of the precuneus are linked to nutritional and functional states in AN, and future longitudinal research should assess how precuneus changes might be related to the evolution of the disorder.

Evaluation of spontaneous regional brain activity in weight-recovered anorexia nervosa

Whereas research using structural magnetic resonance imaging (sMRI) reports sizable grey matter reductions in patients suffering from acute anorexia nervosa (AN) to be largely reversible already after short-term weight gain, many task-based and resting-state functional connectivity (RSFC) studies suggest persistent brain alterations even after long-term weight rehabilitation. First investigations into spontaneous regional brain activity using voxel-wise resting-state measures found widespread abnormalities in acute AN, but no studies have compared intrinsic brain activity properties in weight-recovered individuals with a history of AN (recAN) with healthy controls (HCs). SMRI and RSFC data were analysed from a sample of 130 female volunteers: 65 recAN and 65 pairwise age-matched HC. Cortical grey matter thickness was assessed using FreeSurfer software. Fractional amplitude of low-frequency fluctuations (fALFFs), mean-square successive difference (MSSD), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VHMC), and degree centrality (DC) were calculated. SMRI and RSFC data were analysed from a sample of 130 female volunteers: 65 recAN and 65 pairwise age-matched HCs. Cortical grey matter thickness was assessed using FreeSurfer software. Fractional amplitude of low-frequency fluctuations (fALFF), mean-square successive difference (MSSD), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VHMC), and degree centrality (DC) were calculated. Abnormal regional homogeneity found in acute AN seems to normalize in recAN, supporting assumptions of a state rather than a trait marker. Aberrant fALFF values in the cerebellum and the infertior temporal gyrus could possibly hint towards trait factors or a scar (the latter, e.g., from prolonged periods of undernutrition), warranting further longitudinal research.

Brain structural connectome in relation to PRRT2 mutations in paroxysmal kinesigenic dyskinesia

This study explored the topological characteristics of brain white matter structural networks in patients with Paroxysmal Kinesigenic Dyskinesia (PKD), and the potential influence of the brain network stability gene PRRT2 on the structural connectome in PKD. Thirty-five PKD patients with PRRT2 mutations (PKD-M), 43 PKD patients without PRRT2 mutations (PKD-N), and 40 demographically-matched healthy control (HC) subjects underwent diffusion tensor imaging. Graph theory and network-based statistic (NBS) approaches were performed; the topological properties of the white matter structural connectome were compared across the groups, and their relationships with the clinical variables were assessed. Both disease groups PKD-M and PKD-N showed lower local efficiency (implying decreased segregation ability) compared to the HC group; PKD-M had longer characteristic path length and lower global efficiency (implying decreased integration ability) compared to PKD-N and HC, independently of the potential effects of medication. Both PKD-M and PKD-N had decreased nodal characteristics in the left thalamus and left inferior frontal gyrus, the alterations being more pronounced in PKD-M patients, who also showed abnormalities in the left fusiform and bilateral middle temporal gyrus. In the connectivity characteristics assessed by NBS, the alterations were more pronounced in the PKD-M group versus HC than in PKD-N versus HC. As well as the white matter alterations in the basal ganglia-thalamo-cortical circuit related to PKD with or without PRRT2 mutations, findings in the PKD-M group of weaker small-worldness and more pronounced regional disturbance show the adverse effects of PRRT2 gene mutations on brain structural connectome.

Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data.

Altered global brain network topology as a trait marker in patients with anorexia nervosa

BACKGROUND

Resting state functional magnetic resonance imaging studies have identified functional connectivity patterns associated with acute undernutrition in anorexia nervosa (AN), but few have investigated recovered patients. Thus, a trait connectivity profile characteristic of the disorder remains elusive. Using state-of-the-art graph-theoretic methods in acute AN, the authors previously found abnormal global brain network architecture, possibly driven by local network alterations. To disentangle trait from starvation effects, the present study examines network organization in recovered patients.

METHODS

Graph-theoretic metrics were used to assess resting-state network properties in a large sample of female patients recovered from AN (recAN, n = 55) compared with pairwise age-matched healthy controls (HC, n = 55).

RESULTS

Indicative of an altered global network structure, recAN showed increased assortativity and reduced global clustering as well as small-worldness compared with HC, while no group differences at an intermediate or local network level were evident. However, using support-vector classifier on local metrics, recAN and HC could be separated with an accuracy of 70.4%.

CONCLUSIONS

This pattern of results suggests that long-term recovered patients have an aberrant global brain network configuration, similar to acutely underweight patients. While the finding of increased assortativity may represent a trait marker of AN, the remaining findings could be seen as a scar following prolonged undernutrition.

Toward neuroimaging-based network biomarkers for transient ischemic attack

Stroke is associated with topological disruptions of large-scale functional brain networks. However, whether these disruptions occur in transient ischemic attack (TIA), an important risk factor for stroke, remains largely unknown. Combining multimodal MRI techniques, we systematically examined TIA-related topological alterations of functional brain networks, and tested their reproducibility, structural, and metabolic substrates, associations with clinical risk factors and abilities as diagnostic and prognostic biomarkers. We found that functional networks in patients with TIA exhibited decreased whole-brain network efficiency, reduced nodal centralities in the bilateral insula and basal ganglia, and impaired connectivity of inter-hemispheric communication. These alterations remained largely unchanged when using different brain parcellation schemes or correcting for micro head motion or for regional gray matter volume, cerebral blood flow or hemodynamic lag of BOLD signals in the patients. Moreover, some alterations correlated with the levels of high-density lipoprotein cholesterol (an index related to ischemic attacks via modulation of atherosclerosis) in the patients, distinguished the patients from healthy individuals, and predicted future ischemic attacks in the patients. Collectively, these findings highlight the emergence of characteristic network dysfunctions in TIA, which may aid in elucidating pathological mechanisms and establishing diagnostic and prognostic biomarkers for the disease.

Editorial: Connecting the Nodes of Altered Brain Network Organization in Eating Disorders

Two prevalent eating disorders (ED) in adolescence are anorexia nervosa (AN) and bulimia nervosa (BN). AN is characterized primarily by an extensive restriction of energy intake leading to significantly low body weight. In contrast, the cardinal symptom of BN is uncontrolled eating of an abnormally large amount of food, followed by compensatory behavior to avoid weight gain (eg, self-induced vomiting or laxative abuse). Despite these differences and the fact that individuals with BN are usually of normal weight, patients with both disorders have an abnormal preoccupation with body weight and shape,¹ often in the form of distressing ruminations.^2,3 Left untreated, body image distortion and associated extreme eating/purging habits often have severe and life-long physical, social and psychological consequences. A better understanding of the neurobiological substrates of clinical symptoms may help to improve therapeutic interventions and outcome.⁴.

Psychoradiological patterns of small-world properties and a systematic review of connectome studies of patients with 6 major psychiatric disorders

Background

Brain connectome research based on graph theoretical analysis shows that small-world topological properties play an important role in the structural and functional alterations observed in patients with psychiatric disorders. However, the reported global topological alterations in small-world properties are controversial, are not consistently conceptualized according to agreed-upon criteria, and are not critically examined for consistent alterations in patients with each major psychiatric disorder.

Methods

Based on a comprehensive PubMed search, we systematically reviewed studies using noninvasive neuroimaging data and graph theoretical approaches for 6 major psychiatric disorders: schizophrenia, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD), bipolar disorder (BD), obsessive–compulsive disorder (OCD) and posttraumatic stress disorder (PTSD). Here, we describe the main patterns of altered small-world properties and then systematically review the evidence for these alterations in the structural and functional connectome in patients with these disorders.

Results

We selected 40 studies of schizophrenia, 33 studies of MDD, 5 studies of ADHD, 5 studies of BD, 7 studies of OCD and 5 studies of PTSD. The following 4 patterns of altered small-world properties are defined from theperspectives of segregation and integration: "regularization," "randomization," "stronger small-worldization" and "weaker small-worldization." Although more differences than similarities are noted in patients with these disorders, a prominent trend is the structural regularization versus functional randomization in patients with schizophrenia.

Limitations

Differences in demographic and clinical characteristics, preprocessing steps and analytical methods can produce contradictory results, increasing the difficulty of integrating results across different studies.

Conclusion

Four psychoradiological patterns of altered small-world properties are proposed. The analysis of altered smallworld properties may provide novel insights into the pathophysiological mechanisms underlying psychiatric disorders from a connectomic perspective. In future connectome studies, the global network measures of both segregation and integration should be calculated to fully evaluate altered small-world properties in patients with a particular disease.

Evaluating Functional Connectivity Alterations in Autism Spectrum Disorder Using Network-Based Statistics

The study of resting-state functional brain networks is a powerful tool to understand the neurological bases of a variety of disorders such as Autism Spectrum Disorder (ASD). In this work, we have studied the differences in functional brain connectivity between a group of 74 ASD subjects and a group of 82 typical-development (TD) subjects using functional magnetic resonance imaging (fMRI). We have used a network approach whereby the brain is divided into discrete regions or nodes that interact with each other through connections or edges. Functional brain networks were estimated using the Pearson’s correlation coefficient and compared by means of the Network-Based Statistic (NBS) method. The obtained results reveal a combination of both overconnectivity and underconnectivity, with the presence of networks in which the connectivity levels differ significantly between ASD and TD groups. The alterations mainly affect the temporal and frontal lobe, as well as the limbic system, especially those regions related with social interaction and emotion management functions. These results are concordant with the clinical profile of the disorder and can contribute to the elucidation of its neurological basis, encouraging the development of new clinical approaches.

Altered cerebellar-insular-parietal-cingular subnetwork in adolescents in the earliest stages of anorexia nervosa: a network-based statistic analysis

To date, few functional magnetic resonance imaging (fMRI) studies have explored resting-state functional connectivity (RSFC) in long-lasting anorexia nervosa (AN) patients via graph analysis. The aim of the present study is to investigate, via a graph approach (i.e., the network-based statistic), RSFC in a sample of adolescents at the earliest stages of AN (i.e., AN duration less than 6 months). Resting-state fMRI data was obtained from 15 treatment-naive female adolescents with AN restrictive type (AN-r) in its earliest stages and 15 age-matched healthy female controls. A network-based statistic analysis was used to isolate networks of interconnected nodes that differ between the two groups. Group comparison showed a decreased connectivity in a sub-network of connections encompassing the left and right rostral ACC, left paracentral lobule, left cerebellum (10th sub-division), left posterior insula, left medial fronto-orbital gyrus, and right superior occipital gyrus in AN patients. Results were not associated to alterations in intranodal or global connectivity. No sub-networks with an increased connectivity were identified in AN patients. Our findings suggest that RSFC may be specifically affected at the earliest stages of AN. Considering that the altered sub-network comprises areas mainly involved in somatosensory and interoceptive information and processing and in emotional processes, it could sustain abnormal integration of somatosensory and homeostatic signals, which may explain body image disturbances in AN. Further studies with larger samples and longitudinal designs are needed to confirm our findings and better understand the role and consequences of such functional alterations in AN.

Toward valid and reliable brain imaging results in eating disorders

Human brain imaging can help improve our understanding of mechanisms underlying brain function and how they drive behavior in health and disease. Such knowledge may eventually help us to devise better treatments for psychiatric disorders. However, the brain imaging literature in psychiatry and especially eating disorders has been inconsistent, and studies are often difficult to replicate. The extent or severity of extremes of eating and state of illness, which are often associated with differences in, for instance hormonal status, comorbidity, and medication use, commonly differ between studies and likely add to variation across study results. Those effects are in addition to the well-described problems arising from differences in task designs, data quality control procedures, image data preprocessing and analysis or statistical thresholds applied across studies. Which of those factors are most relevant to improve reproducibility is still a question for debate and further research. Here we propose guidelines for brain imaging research in eating disorders to acquire valid results that are more reliable and clinically useful.

On the Reliability of Individual Brain Activity Networks

There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH₂ network construction method outperforms other approaches at most data spatiotemporal resolutions.

Neural Network Alterations Across Eating Disorders: A Narrative Review of fMRI Studies

BACKGROUND

Functional magnetic resonance imaging (fMRI) has provided insight on how neural abnormalities are related to the symptomatology of the eating disorders (EDs): anorexia nervosa (AN), bulimia nervosa (BN), and binge eating disorder (BED). More specifically, an increasingly growing number of brain imaging studies has shed light on how functionally connected brain networks contribute not only to disturbed eating behavior, but also to transdiagnostic alterations in body/interoceptive perception, reward processing and executive functioning.

METHODS

This narrative review aims to summarize recent advances in fMRI studies of patients with EDs by highlighting studies investigating network alterations that are shared across EDs.

RESULTS AND CONCLUSION

Findings on reward processing in both AN and BN patients point to the presence of altered sensitivity to salient food stimuli in striatal regions and to the possibility of hypothalamic inputs being overridden by top-down emotional-cognitive control regions. Additionally, innovative new lines of research suggest that increased activations in fronto-striatal circuits are strongly associated with the maintenance of restrictive eating habits in AN patients. Although significantly fewer studies have been carried out in patients with BN and BED, aberrant neural responses to both food cues and anticipated food receipt appear to occur in these populations. These altered responses, coupled with diminished recruitment of prefrontal cognitive control circuitry, are believed to contribute to the binge eating of palatable foods. Results from functional network connectivity studies are diverse, but findings tend to converge on indicating disrupted resting-state connectivity in executive networks, the default-mode network and the salience network across EDs.

Reduced Resting-State Functional Connectivity in Current and Recovered Restrictive Anorexia Nervosa

Functional connectivity studies based on resting-state functional magnetic resonance imaging (rs-fMRI) have shown alterations in brain networks associated with self-referential processing, cognitive control, and somatosensory processing in anorexia nervosa (AN). This study aimed to further investigate the functional connectivity of resting-state networks (RSNs) in homogenous subsamples of individuals with restrictive AN (current and recovered) and the relationship this has with core eating disorder psychopathology. rs-fMRI scans were obtained from 12 female individuals with restrictive AN, 14 females recovered from restrictive AN, and 16 female healthy controls. Independent components analysis revealed a set of functionally relevant RSNs, previously reported in the literature. Dual regression analysis showed decreased temporal coherence within the lateral visual and auditory RSNs in individuals with current AN and those recovered from AN compared to healthy individuals. This decreased connectivity was also found in regions associated with somatosensory processing, and is consistent with reduced interoceptive awareness and body image perception, characteristic of AN. Widespread gray matter (GM) reductions were also found in both the AN groups, and differences in functional connectivity were no longer significant when GM maps were added as a covariate in the dual regression analysis. This raises the possibility that deficits in somatosensory and interoceptive processing observed in AN may be in part underpinned or exacerbated by GM reductions.